class_docstr: Reranks a list of hypotheses according to a sentence-level metric.
  
  :param metric: Sentence-level metric such as smoothed BLEU.
  :param return_score: If True, also return the sentence-level score.
  :param isometric_alpha: Factor for reranking with isometric criteria.
class_docstr_tok: ["Reranks","a","list","of","hypotheses","according","to","a","sentence-level","metric",".",":","param","metric",":","Sentence-level","metric","such","as","smoothed","BLEU",".",":","param","return_score",":","If","True",",","also","return","the","sentence-level","score",".",":","param","isometric_alpha",":","Factor","for","reranking","with","isometric","criteria","."]
code: def _get_ranking_indices(
      scores: List, kind: str = "mergesort", order: str = "descending"
  ) -> List:
      if order == "descending":
          return list(np.argsort(scores, kind=kind)[::-1])  # type: ignore
      else:
          return list(np.argsort(scores, kind=kind))
  
file_path: sockeye/sockeye/rerank.py
imports: import argparse
  from functools import partial
  import json
  import logging
  import sys
  from typing import Any, Dict, List
  import numpy
  import sacrebleu
  from .None import arguments
  from .None import constants
  from .None import log
  from .None import utils
label_desc: Use image node_id 3 for calling the Reranker obj's underlying member method code with example usage: obj._get_ranking_indices(scores, kind, order) and returns: list, list